As a result of the greater availability of big data, as well as the decreasing costs and increasing power of modern computing, the use of artificial neural networks for financial time series forecasting is once again a major topic of discussion and research in the financial world. Despite this academic focus, there are still contrasting opinions and bodies of literature on which artificial neural networks perform the best and whether or not they outperform the forecasting capabilities of conventional time series models. This paper uses a long-short term memory network to perform financial time series forecasting on the return data of the JSE Top 40 index. Furthermore, the forecasting performance of the long-short term memory network is compared to the forecasting performance of a seasonal autoregressive integrated moving average model. This paper evaluates the varying approaches presented in the existing literature and ultimately, compares the results to that existing literature. The paper concludes that the long short-term memory network outperforms the seasonal autoregressive integrated moving average model when forecasting intraday directional movements as well as when forecasting the index close price.
The problem of (point) forecasting $ \textit{univariate} $ time series is considered. Most approaches, ranging from traditional statistical methods to recent learning-based techniques with neural networks, directly operate on raw time series observations. As an extension, we study whether $\textit{local topological properties}$, as captured via persistent homology, can serve as a reliable signal that provides complementary information for learning to forecast. To this end, we propose $\textit{topological attention}$, which allows attending to local topological features within a time horizon of…
Matthias Anderer GmbH（Matthias Anderer有限公司）；Central University of Finance and Economics（中央财经大学）
Hierarchical forecasting with intermittent time series is a challenge in both research and empirical studies. Extensive research focuses on improving the accuracy of each hierarchy, especially the intermittent time series at bottom levels. Then hierarchical reconciliation could be used to improve the overall performance further. In this paper, we present a \emph{hierarchical-forecasting-with-alignment} approach that treats the bottom level forecasts as mutable to ensure higher forecasting accuracy on the upper levels of the hierarchy. We employ a pure deep learning forecasting approach N-BEATS…
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；Alibaba Cloud Intelligence Business Group（阿里云智能事业群）
Topological Data Analysis (TDA) is a recent approach to analyze data sets from the perspective of their topological structure. Its use for time series data has been limited. In this work, a system developed for a leading provider of cloud computing combining both user segmentation and demand forecasting is presented. It consists of a TDA-based clustering method for time series inspired by a popular managerial framework for customer segmentation and extended to the case of clusterwise regression using matrix factorization methods to forecast demand. Increasing customer loyalty and producing acc…
Probabilistic, hierarchically coherent forecasting is a key problem in many practical forecasting applications -- the goal is to obtain coherent probabilistic predictions for a large number of time series arranged in a pre-specified tree hierarchy. In this paper, we present an end-to-end deep probabilistic model for hierarchical forecasting that is motivated by a classical top-down strategy. It jointly learns the distribution of the root time series, and the (dirichlet) proportions according to which each parent time-series is split among its children at any point in time. The resulting foreca…
Southern Methodist University（南卫理公会大学）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；NASA Jet Propulsion Laboratory（美国国家航空航天局喷气推进实验室）
There recently has been a surge of interest in developing a new class of deep learning (DL) architectures that integrate an explicit time dimension as a fundamental building block of learning and representation mechanisms. In turn, many recent results show that topological descriptors of the observed data, encoding information on the shape of the dataset in a topological space at different scales, that is, persistent homology of the data, may contain important complementary information, improving both performance and robustness of DL. As convergence of these two emerging ideas, we propose to e…
Robust Short-Term OEE Forecasting in Industry 4.0 via Topological Data Analysis
Institute of Applied Mathematics, Middle East Technical University（应用数学研究所，中东部技术大学）；Network Technologies Department, TÜBİTAK ULAKBİM（网络技术部门，TÜBİTAK ULAKBİM）；Department of Statistics, Middle East Technical University（统计学系，中东部技术大学）
INESC TEC（INESC技术研究所）；University of Porto（波尔图大学）；Dalhousie University（达尔豪斯大学）
Time series forecasting is one of the most active research topics. Machine learning methods have been increasingly adopted to solve these predictive tasks. However, in a recent work, these were shown to systematically present a lower predictive performance relative to simple statistical methods. In this work, we counter these results. We show that these are only valid under an extremely low sample size. Using a learning curve method, our results suggest that machine learning methods improve their relative predictive performance as the sample size grows. The code to reproduce the experiments is…
Cyber-physical systems often consist of entities that interact with each other over time. Meanwhile, as part of the continued digitization of industrial processes, various sensor technologies are deployed that enable us to record time-varying attributes (a.k.a., time series) of such entities, thus producing correlated time series. To enable accurate forecasting on such correlated time series, this paper proposes two models that combine convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The first model employs a CNN on each individual time series, combines the convoluted…
Lancaster University（兰卡斯特大学）；Technical University of Ostrava（俄斯特拉发技术大学）；Sharif University of Technology（谢里夫理工大学）
One of the challenging questions in time series forecasting is how to find the best algorithm. In recent years, a recommender system scheme has been developed for time series analysis using a meta-learning approach. This system selects the best forecasting method with consideration of the time series characteristics. In this paper, we propose a novel approach to focusing on some of the unanswered questions resulting from the use of meta-learning in time series forecasting. Therefore, three main gaps in previous works are addressed including, analyzing various subsets of top forecasters as inpu…
Not All News Is Equal: Topic- and Event-Conditional Sentiment from Finetuned LLMs for Aluminum Price Forecasting
International Business School, Zhejiang University（浙江大学国际商学院）；Center for Data Science, Zhejiang University（浙江大学数据科学中心）；Centre for Human Genetics, Nuffield Department of Medicine, Oxford University（牛津大学纳菲尔德医学部人类遗传学中心）；School of Medicine, Zhejiang University（浙江大学医学院）；Bielefeld School of Business, Hochschule Bielefeld (HSBI) - University of Applied Sciences and Arts（比勒费尔德应用科学大学比勒费尔德商学院）
University of Washington（华盛顿大学）；INRIX, Inc.（因瑞克斯公司）
Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, Traffic Graph Convolutional Long Short-Term Memory Neural Network (TGC-LSTM), to learn the interactions between roadways in the traffic network and forecast the network-wide traffic state. We define the traffic graph convolution based on the physical network topology. The relationship between t…
i-EM S.r.l（i-EM有限公司）；Department of Energy, Systems, Territory and Constructions Engineering, University of Pisa（比萨大学能源、系统、领土和建筑工程系）；Enel Green Power S.p.A.（意大利国家电力公司绿色电力分公司（Enel Green Power S.p.A.））；Renewable Energy Management, Enel Green Power S.p.A.（可再生能源管理，意大利国家电力公司绿色电力分公司（Enel Green Power S.p.A.））；University of Pisa（比萨大学）
The ability to accurately forecast power generation from renewable sources is nowadays recognised as a fundamental skill to improve the operation of power systems. Despite the general interest of the power community in this topic, it is not always simple to compare different forecasting methodologies, and infer the impact of single components in providing accurate predictions. In this paper we extensively compare simple forecasting methodologies with more sophisticated ones over 32 photovoltaic plants of different size and technology over a whole year. Also, we try to evaluate the impact of we…
Time series forecasting is a crucial task in machine learning, as it has a wide range of applications including but not limited to forecasting electricity consumption, traffic, and air quality. Traditional forecasting models rely on rolling averages, vector auto-regression and auto-regressive integrated moving averages. On the other hand, deep learning and matrix factorization models have been recently proposed to tackle the same problem with more competitive performance. However, one major drawback of such models is that they tend to be overly complex in comparison to traditional techniques. …
Due to their widespread adoption, social media platforms present an ideal environment for studying and understanding social behavior, especially on information spread. Modeling social media activity has numerous practical implications such as supporting efforts to analyze strategic information operations, designing intervention techniques to mitigate disinformation, or delivering critical information during disaster relief operations. In this paper we propose a modeling technique that forecasts topic-specific daily volume of social media activities by using both exogenous signals, such as news…
The increasing reliance on online communities for healthcare information by patients and caregivers has led to the increase in the spread of misinformation, or subjective, anecdotal and inaccurate or non-specific recommendations, which, if acted on, could cause serious harm to the patients. Hence, there is an urgent need to connect users with accurate and tailored health information in a timely manner to prevent such harm. This paper proposes an innovative approach to suggesting reliable information to participants in online communities as they move through different stages in their disease or…
We propose here an extended attention model for sequence-to-sequence recurrent neural networks (RNNs) designed to capture (pseudo-)periods in time series. This extended attention model can be deployed on top of any RNN and is shown to yield state-of-the-art performance for time series forecasting on several univariate and multivariate time series.
TOBB University of Economics and Technology（TOBB经济与技术大学）
Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers came up with various models and a vast number of studies have been published accordingly. As such, a significant amount of surveys exist covering ML for financial time series forecasting studies. Lately, Deep Learning (DL) models started appearing within the field, with results that significantly outperform traditional ML counterparts. …
Institute for Intelligent Systems（智能系统研究所）；University of Johannesburg（约翰内斯堡大学）
Meta-learning, decision fusion, hybrid models, and representation learning are topics of investigation with significant traction in time-series forecasting research. Of these two specific areas have shown state-of-the-art results in forecasting: hybrid meta-learning models such as Exponential Smoothing - Recurrent Neural Network (ES-RNN) and Neural Basis Expansion Analysis (N-BEATS) and feature-based stacking ensembles such as Feature-based FORecast Model Averaging (FFORMA). However, a unified taxonomy for model fusion and an empirical comparison of these hybrid and feature-based stacking ense…
In this paper we offer a review and bibliography of work on Hankel low-rank approximation and completion, with particular emphasis on how this methodology can be used for time series analysis and forecasting. We begin by describing possible formulations of the problem and offer commentary on related topics and challenges in obtaining globally optimal solutions. Key theorems are provided, and the paper closes with some expository examples.
University of Science and Technology of China（中国科学技术大学）
In traffic forecasting, graph convolutional networks (GCNs), which model traffic flows as spatio-temporal graphs, have achieved remarkable performance. However, existing GCN-based methods heuristically define the graph structure as the physical topology of the road network, ignoring potential dependence of the graph structure over traffic data. And the defined graph structure is deterministic, which lacks investigation of uncertainty. In this paper, we propose a Bayesian Spatio-Temporal Graph Convolutional Network (BSTGCN) for traffic prediction. The graph structure in our network is learned f…
Beihang University（北京航空航天大学）；Central University of Finance and Economics（中央财经大学）
Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model averaging has been an emerging research focus in the forecasting community. Nonetheless, most of the existing approaches depend on the manual choice of an appropriate set of features. Exploiting machine learning methods to extract features from time series automatically becomes crucial in state-of-the-art time series analysis. In this paper, we introduce an automated approach to extract time series features …
This paper propose DualVDT, a generative model for Time-series forecasting. Introduced dual reparametrized variational mechanisms on variational autoencoder (VAE) to tighter the evidence lower bound (ELBO) of the model, prove the advance performance analytically. This mechanism leverage the latent score based generative model (SGM), explicitly denoising the perturbation accumulated on latent vector through reverse time stochastic differential equation and variational ancestral sampling. The posterior of denoised latent distribution fused with dual reparametrized variational density. The KL div…
Decision-aware training of spatiotemporal forecasting models to select a top K subset of sites for intervention
Department of Computer Science, Tufts University, Medford, Massachusetts, United States（计算机科学系，塔夫茨大学，马萨诸塞州梅德福，美国）；Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, Massachusetts, United States（公共卫生与社区医学系，塔夫茨大学医学院，马萨诸塞州波士顿，美国）
Asymmetric Peak-Aware Loss for Peak-Critical Time Series Forecasting
Department of Transport and Planning, Faculty of Civil Engineering and Geosciences（交通与规划系，土木与地球科学学院）；Mobility Innovation Centre Delft University of Technology（代尔夫特理工大学移动创新中心）；Delft University of Technology（代尔夫特理工大学）
Groundwater is a precious natural resource. Groundwater level (GWL) forecasting is crucial in the field of water resource management. Measurement of GWL from observation-wells is the principle source of information about the aquifer and is critical to its evaluation. Most part of the Udupi district of Karnataka State in India consists of geological formations: lateritic terrain and gneissic complex. Due to the topographical ruggedness and inconsistency in rainfall, the GWL in Udupi region is declining continually and most of the open wells are drying-up during the summer. Hence, the current re…
UiT The Arctic University of Norway（挪威北极大学）；Kongsberg Satellite Services（康斯伯格卫星服务公司）；NORCE Norwegian Research Centre（挪威NORCE研究中心）
This paper presents a novel probabilistic forecasting method called ensemble conformalized quantile regression (EnCQR). EnCQR constructs distribution-free and approximately marginally valid prediction intervals (PIs), which are suitable for nonstationary and heteroscedastic time series data. EnCQR can be applied on top of a generic forecasting model, including deep learning architectures. EnCQR exploits a bootstrap ensemble estimator, which enables the use of conformal predictors for time series by removing the requirement of data exchangeability. The ensemble learners are implemented as gener…
Anticipating price developments in financial markets is a topic of continued interest in forecasting. Funneled by advancements in deep learning and natural language processing (NLP) together with the availability of vast amounts of textual data in form of news articles, social media postings, etc., an increasing number of studies incorporate text-based predictors in forecasting models. We contribute to this literature by introducing weak learning, a recently proposed NLP approach to address the problem that text data is unlabeled. Without a dependent variable, it is not possible to finetune pr…
The financial market trend forecasting method is emerging as a hot topic in financial markets today. Many challenges still currently remain, and various researches related thereto have been actively conducted. Especially, recent research of neural network-based financial market trend prediction has attracted much attention. However, previous researches do not deal with the financial market forecasting method based on LSTM which has good performance in time series data. There is also a lack of comparative analysis in the performance of neural network-based prediction techniques and traditional …
European Commission, Joint Research Centre（欧盟委员会联合研究中心）；Università Ca’ Foscari Venezia（威尼斯大学）
In this paper we employ economic news within a neural network framework to forecast the Italian 10-year interest rate spread. We use a big, open-source, database known as Global Database of Events, Language and Tone to extract topical and emotional news content linked to bond markets dynamics. We deploy such information within a probabilistic forecasting framework with autoregressive recurrent networks (DeepAR). Our findings suggest that a deep learning network based on Long-Short Term Memory cells outperforms classical machine learning techniques and provides a forecasting performance that is…
Hybrid Quantum-Classical Spatiotemporal Forecasting for 3D Cloud Fields
CMA Earth system Modeling and Prediction Center (CEMC)（中国气象局地球系统数值预报中心）；State Key Laboratory of Severe Weather (LaSW)（灾害天气国家重点实验室）；School of Computer Science and Engineering, Sichuan University of Science and Engineering（四川轻化工大学计算机科学与工程学院）；School of Electronic and Communication Engineering, Guangzhou University（广州大学电子与通信工程学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
Time series forecasting has been a widely explored task of great importance in many applications. However, it is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series. In this work, we propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder (BVAE) equipped with diffusion, denoise, and disentanglement, namely D3VAE. Specifically, a coupled diffusion probabilistic model is proposed to augment the time series da…
Aalborg University（奥尔堡大学）；University of Southern California（南加州大学）
Correlated time series (CTS) forecasting plays an essential role in many cyber-physical systems, where multiple sensors emit time series that capture interconnected processes. Solutions based on deep learning that deliver state-of-the-art CTS forecasting performance employ a variety of spatio-temporal (ST) blocks that are able to model temporal dependencies and spatial correlations among time series. However, two challenges remain. First, ST-blocks are designed manually, which is time consuming and costly. Second, existing forecasting models simply stack the same ST-blocks multiple times, whic…
SPINEX-TimeSeries: Similarity-based Predictions with Explainable Neighbors Exploration for Time Series and Forecasting Problems
University of Manitoba（曼尼托巴大学）；Clemson University（克莱姆森大学）；Artificial Intelligence Research Institute for Science and Engineering (AIRISE), Clemson University（克莱姆森大学科学与工程人工智能研究院（AIRISE））
Google Research（谷歌研究院）；Indian Institute of Technology, Madras（印度理工学院马德拉斯分校）
We formulate a new inference task in the domain of multivariate time series forecasting (MTSF), called Variable Subset Forecast (VSF), where only a small subset of the variables is available during inference. Variables are absent during inference because of long-term data loss (eg. sensor failures) or high -> low-resource domain shift between train / test. To the best of our knowledge, robustness of MTSF models in presence of such failures, has not been studied in the literature. Through extensive evaluation, we first show that the performance of state of the art methods degrade significantly …
Distribution feeder long-term load forecast (LTLF) is a critical task many electric utility companies perform on an annual basis. The goal of this task is to forecast the annual load of distribution feeders. The previous top-down and bottom-up LTLF methods are unable to incorporate different levels of information. This paper proposes a hybrid modeling method using sequence prediction for this classic and important task. The proposed method can seamlessly integrate top-down, bottom-up and sequential information hidden in multi-year data. Two advanced sequence prediction models Long Short-Term M…
System States Forecasting of Microservices with Dynamic Spatio-Temporal Data
Institute of Information Engineering, CAS（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Regime-Aware Conditional Neural Processes with Multi-Criteria Decision Support for Operational Electricity Price Forecasting
Faculty of Mathematics and Economics, Ulm University（数学与经济学院，乌尔姆大学）；Department of Mathematics, Natural and Economic Sciences, Ulm University of Applied Sciences（数学、自然与经济科学系，乌尔姆应用科学大学）
